An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks - 2016
Recent advances in environmental energy harvesting technologies have provided great potentials for ancient battery powered sensor networks to attain perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies thus so much have centered on planning and optimizing energy management schemes on single sensor node, however overlooked the impact of spatial variations of energy distribution when sensors work together at totally different locations. To design a strong sensor network, in this paper, we have a tendency to use mobility to bypass communication bottlenecks caused by spatial energy variations. We tend to employ a mobile collector, referred to as SenCar, to gather information from designated sensors and balance energy consumptions within the network. To show spatial-temporal energy variations, we have a tendency to first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we gift a 2-step approach for mobile data collection. First, we adaptively choose a subset of sensor locations where the SenCar stops to gather information packets during a multi-hop fashion. We have a tendency to develop an adaptive algorithm to go looking for nodes based mostly on their energy and guarantee knowledge collection tour length is bounded. Second, we tend to focus on coming up with distributed algorithms to achieve most network utility by adjusting knowledge rates, link scheduling, and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms will converge to optimality terribly quick and validate its convergence in case of node failure. We tend to also show blessings of our framework like it can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.
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